Release Notes

0.8.0

Prelude

From this release, Qiskit Machine Learning requires Qiskit 1.0 or above, with important changes and upgrades, such as the introduction of Quantum Bayesian inference and the migration of a subset of Qiskit Algorithms features to Qiskit Machine Learning. These changes are part of the process to build full compatibility with the version-2 (V2) Qiskit primitives available from version 0.8 of Qiskit Machine Learning. V1 primitives are deprecated and will be removed from version 0.9 (please find more information below).

New Features

  • Added a new class QBayesian that does quantum Bayesian inference on a a quantum circuit representing a Bayesian network with binary random variables.

    The computational complexity is reduced from \(O(nmP(e)^{-1})\) to \(O(n2^{m}P(e)^{-\frac{1}{2}})\) per sample, where n is the number of nodes in the Bayesian network with at most m parents per node and e the evidence.

    At least a quantum circuit that represents the Bayesian network has to be provided. A quantum circuit can be passed in various forms as long as it represents the joint probability distribution of the Bayesian network. Note that QBayesian defines an order for the qubits in the circuit. The last qubit in the circuit will correspond to the most significant bit in the joint probability distribution. For example, if the random variables A, B, and C are entered into the circuit in this order with (A=1, B=0 and C=0), the probability is represented by the probability amplitude of quantum state 001.

    An example for using this class is as follows:

    from qiskit import QuantumCircuit
from qiskit_machine_learning.algorithms import QBayesian

# Define a quantum circuit
qc = QuantumCircuit(...)

# Initialize the framework
qb = QBayesian(qc)

# Perform inference
result = qb.inference(query={...}, evidence={...})

print("Probability of query given evidence:", result)

  • For the new QBayesian class, a tutorial was added. Please refer to:

    • New QBI tutorial that introduces a step-by-step approach for how to do quantum Bayesian inference on a Bayesian network.

  • Added support for using Qiskit Machine Learning with Python 3.12.

  • Migrated essential Qiskit Algorithms features to Qiskit Machine Learning:

    • qiskit_algorithms/gradients -> qiskit_machine_learning/gradients. Note: only the SPSA, parameter-shift and linear-combination-of-unitaries gradients are retained. Other gradient strategies, such as reverse and finite-diff are not incorporated.

    • qiskit_algorithms/optimizers -> qiskit_machine_learning/optimizers. Note: optimizers from scikit-quant are not incorporated.

    • qiskit_algorithms/state_fidelities -> qiskit_machine_learning/state_fidelities

    • Partial merge of qiskit_algorithms/utils with qiskit_machine_learning/utils

    • From the next release, Qiskit Machine Learning will require Qiskit 1.0 or higher. You may be required to upgrade Qiskit Aer accordingly, depending on your set-up.

  • Support for V2 Primitives: The EstimatorQNN and SamplerQNN classes now support V2 primitives (EstimatorV2 and SamplerV2), allowing direct execution on IBM Quantum backends. This enhancement ensures compatibility with Qiskit IBM Runtime’s Primitive Unified Block (PUB) requirements and instruction set architecture (ISA) constraints for circuits and observables. Users can switch between V1 primitives and V2 primitives from version 0.8. From version 0.9, V1 primitives will be removed.

Upgrade Notes

  • Removed support for using Qiskit Machine Learning with Python 3.8 to reflect the EOL of Python 3.8 in October 2024 (PEP 569). To continue using Qiskit Machine Learning, you must upgrade to a Python: 3.9 or above if you are using older versions of Python.

  • From version 0.8.0, Qiskit Machine Learning requires Qiskit 1.0 or higher.

  • The merge of some of the features of Qiskit Algorithms into Qiskit Machine Learning might lead to breaking changes. For this reason, caution is advised when updating to version 0.8 during critical production stages in a project. This change ensures continued enhancement and maintenance of essential features for Qiskit Machine Learning following the end of official support for Qiskit Algorithms. Therefore, Qiskit Machine Learning will no longer depend on Qiskit Algorithms.

  • Users must update their imports and code references in code that uses Qiskit Machine Leaning and Algorithms:

    • Change qiskit_algorithms.gradients to qiskit_machine_learning.gradients

    • Change qiskit_algorithms.optimizers to qiskit_machine_learning.optimizers

    • Change qiskit_algorithms.state_fidelities to qiskit_machine_learning.state_fidelities

    • Update utilities as needed due to partial merge.

  • To continue using sub-modules and functionalities of Qiskit Algorithms that have not been transferred, you may continue using them as before by importing from Qiskit Algorithms. However, be aware that Qiskit Algorithms is no longer officially supported and some of its functionalities may not work in your use case. For any problems directly related to Qiskit Algorithms, please open a GitHub issue at https://github .com/qiskit-community/qiskit-algorithms. Should you want to include a Qiskit Algorithms functionality that has not been incorporated in Qiskit Machine Learning, please open a feature-request issue at https://github.com/qiskit-community/qiskit-machine-learning, explaining why this change would be useful for you and other users.

  • Four examples of upgrading the code can be found below.

    Gradients:

    # Before:
from qiskit_algorithms.gradients import SPSA, ParameterShift
# After:
from qiskit_machine_learning.gradients import SPSA, ParameterShift
# Usage
spsa = SPSA()
param_shift = ParameterShift()

    Optimizers

    # Before:
from qiskit_algorithms.optimizers import COBYLA, ADAM
# After:
from qiskit_machine_learning.optimizers import COBYLA, ADAM
# Usage
cobyla = COBYLA()
adam = ADAM()

    Quantum state fidelities

    # Before:
from qiskit_algorithms.state_fidelities import ComputeFidelity
# After:
from qiskit_machine_learning.state_fidelities import ComputeFidelity
# Usage
fidelity = ComputeFidelity()

    Algorithm globals (used to fix the random seed)

    # Before:
from qiskit_algorithms.utils import algorithm_globals
# After:
from qiskit_machine_learning.utils import algorithm_globals
algorithm_globals.random_seed = 1234

  • Users working with real backends are advised to migrate to V2 primitives (EstimatorV2 and SamplerV2) to ensure compatibility with Qiskit IBM Runtime hardware requirements. These V2 primitives will become the standard in the 0.8 release going forward, while V1 primitives are deprecated.

Deprecation Notes

  • Deprecated V1 Primitives: The V1 primitives (e.g., EstimatorV1 and SamplerV1) are no longer compatible with real quantum backends via Qiskit IBM Runtime. This update provides initial transitional support, but V1 primitives may be fully deprecated and removed in version 0.9. Users should adopt V2 primitives for both local and hardware executions to ensure long-term compatibility.

Bug Fixes

  • Added a max_circuits_per_job parameter to the FidelityQuantumKernel used in the case that if more circuits are submitted than the job limit for the backend, the circuits are split up and run through separate jobs.

  • Fixes the dimension mismatch error in the torch_connector raised when using other-than 3D datasets. The updated implementation defines the Einstein summation signature dynamically based on the number of dimensions ndim of the input data (up to 26 dimensions).

  • Fixes an issue for the Quantum Neural Networks where the binding order of the inputs and weights might end up being incorrect. Though the params for the inputs and weights are specified to the QNN, the code previously bound the inputs and weights in the order given by the circuit.parameters. This would end up being the right order for the Qiskit circuit library feature maps and ansatzes most often used, as the default parameter names led to the order being as expected. However for custom names etc. this was not always the case and then led to unexpected behavior. The sequences for the input and weights parameters, as supplied, are now always used as the binding order, for the inputs and weights respectively, such that the order of the parameters in the overall circuit no longer matters.

0.7.0

Prelude

Qiskit Machine Learning has been migrated to the qiskit-community Github organization to further emphasize that it is a community-driven project. To reflect this change, and because we are onboarding additional code-owners and maintainers, with this version (0.7) we have decided to remove all deprecated code, regardless of the time of its deprecation. This ensures that the new members of the development team do not have a large bulk of legacy code to maintain. This can mean one of two things for you as the end-user:

  1. Nothing, if you already migrated your code and no longer rely on any deprecated features.

  2. Otherwise, you should make sure that your workflow doesn’t rely on deprecated classes. If you cannot do that, or want to continue using some of the features that were removed, you should pin your version of Qiskit Machine Learning to 0.6.

For more context on the changes around Qiskit Machine Learning and the other application projects as well as the Algorithms library in Qiskit, be sure to read this blog post.

New Features

  • The QNNCircuit class can be passed as circuit to the SamplerQNN and EstimatorQNN. This simplifies the interfaces to build a Sampler or Estimator based neural network implementation from a feature map and an ansatz circuit.

    Using the QNNCircuit comes with the benefit that the feature map and ansatz do not have to be composed explicitly. If a QNNCircuit is passed to the SamplerQNN or EstimatorQNN the input and weight parameters do not have to be provided, because these two properties are taken from the QNNCircuit.

    An example of using QNNCircuit with the SamplerQNN class is as follows:

    from qiskit_machine_learning.circuit.library import QNNCircuit
from qiskit_machine_learning.neural_networks import SamplerQNN

def parity(x):
    return f"{bin(x)}".count("1") % 2

# Create a parameterized 2 qubit circuit composed of the default ZZFeatureMap feature map
# and RealAmplitudes ansatz.
qnn_qc = QNNCircuit(num_qubits = 2)

qnn = SamplerQNN(
    circuit=qnn_qc,
    interpret=parity,
    output_shape=2
)

qnn.forward(input_data=[1, 2], weights=[1, 2, 3, 4, 5, 6, 7, 8])

    The QNNCircuit is used with the EstimatorQNN class in the same fashion:

    from qiskit_machine_learning.circuit.library import QNNCircuit
from qiskit_machine_learning.neural_networks import EstimatorQNN

# Create a parameterized 2 qubit circuit composed of the default ZZFeatureMap feature map
# and RealAmplitudes ansatz.
qnn_qc = QNNCircuit(num_qubits = 2)

qnn = EstimatorQNN(
    circuit=qnn_qc
)

qnn.forward(input_data=[1, 2], weights=[1, 2, 3, 4, 5, 6, 7, 8])

  • Added a new QNNCircuit class that composes a Quantum Circuit from a feature map and an ansatz.

    At least one parameter, i.e. number of qubits, feature map, ansatz, has to be provided.

    If only the number of qubits is provided the resulting quantum circuit is a composition of the ZZFeatureMap and the RealAmplitudes ansatz. If the number of qubits is 1 the ZFeatureMap is used per default. If only a feature map is provided, the RealAmplitudes ansatz with the corresponding number of qubits is used. If only an ansatz is provided the ZZFeatureMap with the corresponding number of qubits is used.

    In case number of qubits is provided along with either a feature map, an ansatz or both, a potential mismatch between the three inputs with respect to the number of qubits is resolved by constructing the QNNCircuit with the given number of qubits. If one of the QNNCircuit properties is set after the class construction, the circuit is is adjusted to incorporate the changes. This is, a new valid configuration that considers the latest property update will be derived. This ensures that the classes properties are consistent at all times.

    An example of using this class is as follows:

    from qiskit_machine_learning.circuit.library import QNNCircuit
qnn_qc = QNNCircuit(2)
print(qnn_qc)
# prints:
#      ┌──────────────────────────┐»
# q_0: ┤0                         ├»
#      │  ZZFeatureMap(x[0],x[1]) │»
# q_1: ┤1                         ├»
#      └──────────────────────────┘»
# «     ┌──────────────────────────────────────────────────────────┐
# «q_0: ┤0                                                         ├
# «     │  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7]) │
# «q_1: ┤1                                                         ├
# «     └──────────────────────────────────────────────────────────┘

print(qnn_qc.num_qubits)
# prints: 2

print(qnn_qc.input_parameters)
# prints: ParameterView([ParameterVectorElement(x[0]), ParameterVectorElement(x[1])])

print(qnn_qc.weight_parameters)
# prints: ParameterView([ParameterVectorElement(θ[0]), ParameterVectorElement(θ[1]),
#         ParameterVectorElement(θ[2]), ParameterVectorElement(θ[3]),
#         ParameterVectorElement(θ[4]), ParameterVectorElement(θ[5]),
#         ParameterVectorElement(θ[6]), ParameterVectorElement(θ[7])])

  • A new TrainableFidelityStatevectorKernel class has been added that provides a trainable version of FidelityStatevectorKernel. This relationship mirrors that between the existing FidelityQuantumKernel. Thus, TrainableFidelityStatevectorKernel inherits from both FidelityStatevectorKernel and TrainableKernel.

    This class is used with QuantumKernelTrainer in an identical way to TrainableFidelityQuantumKernel, except for the arguments specific to TrainableFidelityStatevectorKernel.

    For an example, see the snippet below:

    from qiskit.quantum_info import Statevector
from qiskit_machine_learning.kernels import TrainableFidelityStatevectorKernel
from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer

# Instantiate trainable fidelity statevector kernel.
quantum_kernel = TrainableFidelityStatevectorKernel(
    feature_map=<your_feature_map>,
    statevector_type=Statevector,
    training_parameters=<your_training_parameters>,
    cache_size=None,
    auto_clear_cache=True,
    shots=None,
    enforce_psd=True,
)

# Instantiate a quantum kernel trainer (QKT).
qkt = QuantumKernelTrainer(quantum_kernel=quantum_kernel)

# Train the kernel using QKT directly.
qkt_results = qkt.fit(<your_X_train>, <your_y_train>)
optimized_kernel = qkt_results.quantum_kernel

  • The module is migrated to Qiskit Algorithms from the qiskit.algorithms package that is deprecated now.

Upgrade Notes

  • Support for running with Python 3.7 has been removed. To run Qiskit Machine Learning you need a minimum Python version of 3.8.

  • The previously deprecated qgan and runtime packages have been removed. Please refer to:

Bug Fixes

  • Compatibility fix to support Python 3.11.

  • Fixes a bug in FidelityStatevectorKernel where kernel entries could potentially have nonzero complex components due to truncation and rounding errors when enforcing a PSD matrix.

  • Fixed incorrect type conversions in TorchConnector. The bug was causing the connector to convert the output to the same type as the input data. As a result, when an integer tensor was passed, the output would also be converted to an integer tensor, leading to rounding errors.

0.6.0

New Features

  • Allow callable as an optimizer in NeuralNetworkClassifier, VQC, NeuralNetworkRegressor, VQR, as well as in QuantumKernelTrainer.

    Now, the optimizer can either be one of Qiskit’s optimizers, such as SPSA or a callable with the following signature:

    from qiskit.algorithms.optimizers import OptimizerResult

def my_optimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
    # Args:
    #     fun (callable): the function to minimize
    #     x0 (np.ndarray): the initial point for the optimization
    #     jac (callable, optional): the gradient of the objective function
    #     bounds (list, optional): a list of tuples specifying the parameter bounds
    result = OptimizerResult()
    result.x = # optimal parameters
    result.fun = # optimal function value
    return result

    The above signature also allows to directly pass any SciPy minimizer, for instance as

    from functools import partial
from scipy.optimize import minimize
optimizer = partial(minimize, method="L-BFGS-B")

  • Added a new FidelityStatevectorKernel class that is optimized to use only statevector-implemented feature maps. Therefore, computational complexity is reduced from \(O(N^2)\) to \(O(N)\).

    Computed statevector arrays are also cached to further increase efficiency. This cache is cleared when the evaluate method is called, unless auto_clear_cache is False. The cache is unbounded by default, but its size can be set by the user, i.e., limited to the number of samples in the worst case.

    By default the Terra reference Statevector is used, however, the type can be specified via the statevector_type argument.

    Shot noise emulation can also be added. If shots is None, the exact fidelity is used. Otherwise, the mean is taken of samples drawn from a binomial distribution with probability equal to the exact fidelity.

    With the addition of shot noise, the kernel matrix may no longer be positive semi-definite (PSD). With enforce_psd set to True this condition is enforced.

    An example of using this class is as follows:

    from sklearn.datasets import make_blobs
from sklearn.svm import SVC

from qiskit.circuit.library import ZZFeatureMap
from qiskit.quantum_info import Statevector

from qiskit_machine_learning.kernels import FidelityStatevectorKernel

# generate a simple dataset
features, labels = make_blobs(
    n_samples=20, centers=2, center_box=(-1, 1), cluster_std=0.1
)

feature_map = ZZFeatureMap(feature_dimension=2, reps=2)
statevector_type = Statevector

kernel = FidelityStatevectorKernel(
    feature_map=feature_map,
    statevector_type=Statevector,
    cache_size=len(labels),
    auto_clear_cache=True,
    shots=1000,
    enforce_psd=True,
)
svc = SVC(kernel=kernel.evaluate)
svc.fit(features, labels)

  • The PyTorch connector TorchConnector now fully supports sparse output in both forward and backward passes. To enable sparse support, first of all, the underlying quantum neural network must be sparse. In this case, if the sparse property of the connector itself is not set, then the connector inherits sparsity from the networks. If the connector is set to be sparse, but the network is not, an exception will be raised. Also you may set the connector to be dense if the network is sparse.

    This snippet illustrates how to create a sparse instance of the connector.

    import torch
from qiskit import QuantumCircuit
from qiskit.circuit.library import ZFeatureMap, RealAmplitudes

from qiskit_machine_learning.connectors import TorchConnector
from qiskit_machine_learning.neural_networks import SamplerQNN

num_qubits = 2
fmap = ZFeatureMap(num_qubits, reps=1)
ansatz = RealAmplitudes(num_qubits, reps=1)
qc = QuantumCircuit(num_qubits)
qc.compose(fmap, inplace=True)
qc.compose(ansatz, inplace=True)

qnn = SamplerQNN(
    circuit=qc,
    input_params=fmap.parameters,
    weight_params=ansatz.parameters,
    sparse=True,
)

connector = TorchConnector(qnn)

output = connector(torch.tensor([[1., 2.]]))
print(output)

loss = torch.sparse.sum(output)
loss.backward()

grad = connector.weight.grad
print(grad)

    In hybrid setup, where a PyTorch-based neural network has classical and quantum layers, sparse operations should not be mixed with dense ones, otherwise exceptions may be thrown by PyTorch.

    Sparse support works on python 3.8+.

Upgrade Notes

  • The previously deprecated CrossEntropySigmoidLoss loss function has been removed.

  • The previously deprecated datasets have been removed: breast_cancer, digits, gaussian, iris, wine.

  • Positional arguments in QSVC and QSVR were deprecated as of version 0.3. Support of the positional arguments was completely removed in this version, please replace them with corresponding keyword arguments.

Bug Fixes

  • SamplerQNN can now correctly handle quantum circuits without both input parameters and weights. If such a circuit is passed to the QNN then this circuit executed once in the forward pass and backward returns None for both gradients.

0.5.0

New Features

  • Added support for categorical and ordinal labels to VQC. Now labels can be passed in different formats, they can be plain ordinal labels, a one dimensional array that contains integer labels like 0, 1, 2, …, or an array with categorical string labels. One-hot encoded labels are still supported. Internally, labels are transformed to one hot encoding and the classifier is always trained on one hot labels.

  • Introduced Estimator Quantum Neural Network (EstimatorQNN) based on (runtime) primitives. This implementation leverages the estimator primitive (see BaseEstimator) and the estimator gradients (see BaseEstimatorGradient) to enable runtime access and more efficient computation of forward and backward passes.

    The new EstimatorQNN exposes a similar interface to the Opflow QNN, with a few differences. One is the quantum_instance parameter. This parameter does not have a direct replacement, and instead the estimator parameter must be used. The gradient parameter keeps the same name as in the Opflow QNN implementation, but it no longer accepts Opflow gradient classes as inputs; instead, this parameter expects an (optionally custom) primitive gradient.

    The existing training algorithms such as VQR, that were based on the Opflow QNN, are updated to accept both implementations. The implementation of NeuralNetworkRegressor has not changed.

    For example a VQR using EstimatorQNN can be trained as follows:

    import numpy as np

from qiskit.algorithms.optimizers import L_BFGS_B
from qiskit.circuit import QuantumCircuit, Parameter
from qiskit.primitives import Estimator

from qiskit_machine_learning.algorithms import VQR

num_samples = 20
eps = 0.2
lb, ub = -np.pi, np.pi
X = (ub - lb) * np.random.rand(num_samples, 1) + lb
Y = np.sin(X[:, 0]) + eps * (2 * np.random.rand(num_samples) - 1)

params = [Parameter("θ_0"), Parameter("θ_1")]
feature_map = QuantumCircuit(1, name="fm")
feature_map.ry(params[0], 0)
ansatz = QuantumCircuit(1, name="vf")
ansatz.ry(params[1], 0)

vqr = VQR(
    feature_map=feature_map,
    ansatz=ansatz,
    optimizer=L_BFGS_B(maxiter=5),
    initial_point=np.array([0]),
    estimator=Estimator()
)
vqr.fit(X, Y)

  • Introduced Quantum Kernels based on (runtime) primitives. This implementation leverages the fidelity primitive (see BaseStateFidelity) and provides more flexibility to end users. The fidelity primitive calculates state fidelities/overlaps for pairs of quantum circuits and requires an instance of Sampler. Thus, users may plug in their own implementations of fidelity calculations.

    The new kernels expose the same interface and the same parameters except the quantum_instance parameter. This parameter does not have a direct replacement and instead the fidelity parameter must be used.

    A new hierarchy is introduced:

    • A base and abstract class BaseKernel is introduced. All concrete implementation must inherit this class.

    • A fidelity based quantum kernel FidelityQuantumKernel is added. This is a direct replacement of QuantumKernel. The difference is that the new class takes either a sampler or a fidelity instance to estimate overlaps and construct kernel matrix.

    • A new abstract class TrainableKernel is introduced to generalize ability to train quantum kernels.

    • A fidelity-based trainable quantum kernel TrainableFidelityQuantumKernel is introduced. This is a replacement of the existing QuantumKernel if a trainable kernel is required. The trainer QuantumKernelTrainer now accepts both quantum kernel implementations, the new one and the existing one.

    The existing algorithms such as QSVC, QSVR and other kernel-based algorithms are updated to accept both implementations.

    For example a QSVM classifier can be trained as follows:

    from qiskit.algorithms.state_fidelities import ComputeUncompute
from qiskit.circuit.library import ZZFeatureMap
from qiskit.primitives import Sampler
from sklearn.datasets import make_blobs

from qiskit_machine_learning.algorithms import QSVC
from qiskit_machine_learning.kernels import FidelityQuantumKernel

# generate a simple dataset
features, labels = make_blobs(n_samples=20, centers=2, center_box=(-1, 1), cluster_std=0.1)

# fidelity is optional and quantum kernel will create it automatically if none is passed
fidelity = ComputeUncompute(sampler=Sampler())

feature_map = ZZFeatureMap(2)
kernel = FidelityQuantumKernel(feature_map=feature_map, fidelity=fidelity)
qsvc = QSVC(quantum_kernel=kernel)
qsvc.fit(features, labels)

  • Introduced Sampler Quantum Neural Network (SamplerQNN) based on (runtime) primitives. This implementation leverages the sampler primitive (see BaseSampler) and the sampler gradients (see BaseSamplerGradient) to enable runtime access and more efficient computation of forward and backward passes more efficiently.

    The new SamplerQNN exposes a similar interface to the CircuitQNN, with a few differences. One is the quantum_instance parameter. This parameter does not have a direct replacement, and instead the sampler parameter must be used. The gradient parameter keeps the same name as in the CircuitQNN implementation, but it no longer accepts Opflow gradient classes as inputs; instead, this parameter expects an (optionally custom) primitive gradient. The sampling option has been removed for the time being, as this information is not currently exposed by the Sampler, and might correspond to future lower-level primitives.

    The existing training algorithms such as VQC, that were based on the CircuitQNN, are updated to accept both implementations. The implementation of NeuralNetworkClassifier has not changed.

    For example a VQC using SamplerQNN can be trained as follows:

    from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes
from qiskit.algorithms.optimizers import COBYLA
from qiskit.primitives import Sampler
from sklearn.datasets import make_blobs

from qiskit_machine_learning.algorithms import VQC

# generate a simple dataset
num_inputs = 20
features, labels = make_blobs(n_samples=num_inputs, centers=2, center_box=(-1, 1), cluster_std=0.1)

# construct feature map
feature_map = ZZFeatureMap(num_inputs)

# construct ansatz
ansatz = RealAmplitudes(num_inputs, reps=1)

# construct variational quantum classifier
vqc = VQC(
  sampler=sampler,
  feature_map=feature_map,
  ansatz=ansatz,
  loss="cross_entropy",
  optimizer=COBYLA(maxiter=30),
)

# fit classifier to data
vqc.fit(features, labels)

  • Expose the callback attribute as public property on TrainableModel. This, for instance, allows setting the callback between optimizations and store the history in separate objects.

  • Gradient operator/circuit initialization in OpflowQNN and CircuitQNN respectively is now delayed until the first call of the backward method. Thus, the networks are created faster and gradient framework objects are not created until they are required.

  • Introduced a new parameter evaluate_duplicates in QuantumKernel. This parameter defines a strategy how kernel matrix elements are evaluated if duplicate samples are found. Possible values are:

    • all means that all kernel matrix elements are evaluated, even the diagonal ones when

      training. This may introduce additional noise in the matrix.

    • off_diagonal when training the matrix diagonal is set to 1, the rest elements are

      fully evaluated, e.g., for two identical samples in the dataset. When inferring, all elements are evaluated. This is the default value.

    • none when training the diagonal is set to 1 and if two identical samples are found

      in the dataset the corresponding matrix element is set to 1. When inferring, matrix elements for identical samples are set to 1.

  • In the previous releases, in the QGAN class, the gradient penalty could not be enabled to train the discriminator with a penalty function. Thus, a gradient penalty parameter was added during the initialization of the QGAN algorithm. This parameter indicates whether or not penalty function is applied to the loss function of the discriminator during training.

  • Enable the default construction of the ZFeatureMap in the TwoLayerQNN if the number of qubits is 1. Previously, not providing a feature map for the single qubit case raised an error as default construction assumed 2 or more qubits.

  • VQC will now raise an error when training from a warm start when encountering targets with a different number of classes to the previous dataset.

  • VQC will now raise an error when a user attempts multi-label classification, which is not supported.

  • Added two new properties to the TrainableModel class:

    • fit_result returns a resulting object from the optimization procedure. Please refers to the Terra’s documentation of the OptimizerResult class.

    • weights returns an array of trained weights, this is a convenient way to get access to the weights, it is the same as calling model.fit_result.x.

Upgrade Notes

  • The method fit() is not abstract any more. Now, it implements basic checks, calls a new abstract method _fit_internal() to be implemented by sub-classes, and keeps track of fit_result property that is returned by this new abstract method. Thus, any sub-class of TrainableModel must implement this new method. Classes NeuralNetworkClassifier and NeuralNetworkRegressor have been updated correspondingly.

  • Inheriting from sklearn.svm.SVC in PegasosQSVC resulted in errors when calling some inherited methods such as decision_function due to the overridden fit implementation. For that reason, the inheritance has been replaced by a much lighter inheritance from ClassifierMixin providing the score method and a new method decision_function has been implemented. The class is still sklearn compatible due to duck typing. This means that for the user everything that has been working in the previous release still works, except the inheritance. The only methods that are no longer supported (such as predict_proba) were only raising errors in the previous release in practice.

Deprecation Notes

  • The qiskit_machine_learning.algorithms.distribution_learners package is deprecated and will be removed no sooner than 3 months after the release. There’s no direct replacement for the classes from this package. Instead, please refer to the new QGAN tutorial. This tutorial introduces step-by-step how to build a PyTorch-based QGAN using quantum neural networks.

  • Classes qiskit_machine_learning.runtime.TorchRuntimeClient, qiskit_machine_learning.runtime.TorchRuntimeResult, qiskit_machine_learning.runtime.HookBase and functions qiskit_machine_learning.runtime.str_to_obj(), qiskit_machine_learning.runtime.obj_to_str() are being deprecated. You should use QiskitRuntimeService to leverage primitives and runtimes.

  • For the class QuantumKernel, to improve usability and better describe the usage, user_parameters has been renamed to training_parameters; current behavior is retained. For this change the constructor parameter user_parameters is now deprecated and replaced by training_parameters. The related properties and methods are renamed to match. That is to say:

    • argument user_parameters -> training_parameters

    • property user_parameters -> training_parameters

    • property user_param_binds -> training_parameter_binds

    • method assign_user_parameters -> assign_training_parameters

    • method bind_user_parameters -> bind_training_parameters

    • method get_unbound_user_parameters -> get_unbound_training_parameters

Bug Fixes

  • Previously in the QuantumGenerator of the QGAN algorithm, if we used a simulator other than the statevector_simulator the result dictionary had not the correct size to compute both the gradient and the loss functions. Now, the values output are stored in a vector of size 2^n and each key is mapped to its value from the result dictionary in the new value array. Also, each key is stored in a vector of size 2^n where each element of the vector keys[i] corresponds to the binary representation of i.

  • Previously in the QuantumGenerator of the QGAN algorithm, the gradients were computed using the statevector backend even if we specified another backend. To solve this issue, the gradient object is converted into a CircuitStateFn instead of its adjoint as in the previous version. The gradients are converted into the backend-dependent structure using CircuitSampler. After the evaluation of the object, the gradient_function is stored in a dense array to fix a dimension incompatibility when computing the loss function.

  • Fixed quantum kernel evaluation when duplicate samples are found in the dataset. Originally, kernel matrix elements were not evaluated for identical samples in the dataset and such elements were set wrongly to zero. Now we introduced a new parameter evaluate_duplicates that ensures that elements of the kernel matrix are evaluated correctly. See the feature section for more details.

  • Previously in the pytorch_discriminator class of the QGAN algorithm, if the gradient penalty parameter was enabled, the latent variable z was not properly initialized : Variable module was used instead of torch.autograd.Variable.

  • Calling PegasosQSVC.decision_function() raises an error. Fixed by writing own method instead of inheriting from SVC. The inheritance from SVC in the PegasosQSVC class is removed. To keep the score method, inheritance to the mixin class ClassifierMixin from scikit-learn is added.

0.4.0

New Features

  • In the previous releases at the backpropagation stage of CircuitQNN and OpflowQNN gradients were computed for each sample in a dataset individually and then the obtained values were aggregated into one output array. Thus, for each sample in a dataset at least one job was submitted. Now, gradients are computed for all samples in a dataset in one go by passing a list of values for a single parameter to CircuitSampler. Therefore, a number of jobs required for such computations is significantly reduced. This improvement may speed up training process in the cloud environment, where queue time for submitting a job may be a major contribution in the overall training time.

  • Introduced two new classes, EffectiveDimension and LocalEffectiveDimension, for calculating the capacity of quantum neural network models through the computation of the Fisher Information Matrix. The local effective dimension bounds the generalization error of QNNs and only accepts single parameter sets as inputs. The global effective dimension (or just effective dimension) can be used as a measure of the expressibility of the model, and accepts multiple parameter sets.

  • Objective functions constructed by the neural network classifiers and regressors now include an averaging factor that is evaluated as 1 / number_of_samples. Computed averaged objective values are passed to a user specified callback if any. Users may notice a dramatic decrease in the objective values in their callbacks. This is due to this averaging factor.

  • Added support for saving and loading machine learning models. This support is introduced in TrainableModel, so all sub-classes can be saved and loaded. Also, kernel based models can be saved and loaded. A list of models that support saving and loading models:

    • NeuralNetworkClassifier

    • NeuralNetworkRegressor

    • VQC

    • VQR

    • QSVC

    • QSVR

    • PegasosQSVC

    When model is saved all model parameters are saved to a file, including a quantum instance that is referenced by internal objects. That means if a model is loaded from a file and is used, for instance, for inference, the same quantum instance and a corresponding backend will be used even if a cloud backend was used.

  • Added a new feature in CircuitQNN that ensures unbound_pass_manager is called when caching the QNN circuit and that bound_pass_manager is called when QNN parameters are assigned.

  • Added a new feature in QuantumKernel that ensures the bound_pass_manager is used, when provided via the QuantumInstance, when transpiling the kernel circuits.

Upgrade Notes

  • Added support for running with Python 3.10. At the the time of the release, Torch didn’t have a python 3.10 version.

  • The previously deprecated BaseBackend class has been removed. It was originally deprecated in the Qiskit Terra 0.18.0 release.

  • Support for running with Python 3.6 has been removed. To run Machine Learning you need a minimum Python version of 3.7.

Deprecation Notes

  • The functions breast_cancer, digits, gaussian, iris and wine in the datasets module are deprecated and should not be used.

  • Class CrossEntropySigmoidLoss is deprecated and marked for removal.

  • Removed support of l2 and l1 values as loss function definitions. Please, use absolute_error and squared_error respectively.

Bug Fixes

  • Fixes in Ad Hoc dataset. Fixed an ValueError when n=3 is passed to ad_hoc_data. When the value of n is not 2 or 3, a ValueError is raised with a message that the only supported values of n are 2 and 3.

  • Previously, VQC would throw an error if trained on batches of data where not all of the target labels that can be found in the full dataset were present. This is because VQC interpreted the number of unique targets in the current batch as the number of classes. Currently, VQC is hard-coded to expect one-hot-encoded targets. Therefore, VQC will now determine the number of classes from the shape of the target array.

  • Fixes an issue where VQC could not be trained on multiclass datasets. It returned nan values on some iterations. This is fixed in 2 ways. First, the default parity function is now guaranteed to be able to assign at least one output bitstring to each class, so long as 2**N >= C where N is the number of output qubits and C is the number of classes. This guarantees that it is at least possible for every class to be predicted with a non-zero probability. Second, even with this change it is still possible that on a given training instance a class is predicted with 0 probability. Previously this could lead to nan in the CrossEntropyLoss calculation. We now replace 0 probabilities with a small positive value to ensure the loss cannot return nan.

  • Fixes an issue in QuantumKernel where evaluating a quantum kernel for data with dimension d>2 raised an error. This is fixed by changing the hard-coded reshaping of one-dimensional arrays in QuantumKernel.evaluate().

  • Fixes an issue where VQC would fail with warm_start=True. The extraction of the initial_point in TrainableModel from the final point of the minimization had not been updated to reflect the refactor of optimizers in qiskit-terra; the old optimize method, that returned a tuple was deprecated and new method minimize was created that returns an OptimizerResult object. We now correctly recover the final point of the minimization from previous fits to use for a warm start in subsequent fits.

  • Added GPU support to TorchConnector. Now, if a hybrid PyTorch model is being trained on GPU, TorchConnector correctly detaches tensors, moves them to CPU, evaluate forward and backward passes and places resulting tensors to the same device they came from.

  • Fixed a bug when a sparse array is passed to VQC as labels. Sparse arrays can be easily observed when labels are encoded via OneHotEncoder from SciKit-Learn. Now both NeuralNetworkClassifier and VQC support sparse arrays and convert them dense arrays in the implementation.

0.3.0

New Features

  • Addition of a QuantumKernelTrainer object which may be used by kernel-based machine learning algorithms to perform optimization of some QuantumKernel parameters before training the model. Addition of a new base class, KernelLoss, in the loss_functions package. Addition of a new KernelLoss subclass, SVCLoss.

  • The class TrainableModel, and its sub-classes NeuralNetworkClassifier, NeuralNetworkRegressor, VQR, VQC, have a new optional argument callback. User can optionally provide a callback function that can access the intermediate training data to track the optimization process, else it defaults to None. The callback function takes in two parameters: the weights for the objective function and the computed objective value. For each iteration an optimizer invokes the callback and passes current weights and computed value of the objective function.

  • Classification models (i.e. models that extend the NeuralNetworkClassifier class like VQC) can now handle categorical target data in methods like fit() and score(). Categorical data is inferred from the presence of string type data and is automatically encoded using either one-hot or integer encodings. Encoder type is determined by the one_hot argument supplied when instantiating the model.

  • There’s an additional transpilation step introduced in CircuitQNN that is invoked when a quantum instance is set. A circuit passed to CircuitQNN is transpiled and saved for subsequent usages. So, every time when the circuit is executed it is already transpiled and overall time of the forward pass is reduced. Due to implementation limitations of RawFeatureVector it can’t be transpiled in advance, so it is transpiled every time it is required to be executed and only when all parameters are bound. This means overall performance when RawFeatureVector is used stays the same.

  • Introduced a new classification algorithm, which is an alternative version of the Quantum Support Vector Classifier (QSVC) that is trained via the Pegasos algorithm from https://home.ttic.edu/~nati/Publications/PegasosMPB.pdf instead of the dual optimization problem like in sklearn. This algorithm yields a training complexity that is independent of the size of the training set (see the to be published Master’s Thesis “Comparing Quantum Neural Networks and Quantum Support Vector Machines” by Arne Thomsen), such that the PegasosQSVC is expected to train faster than QSVC for sufficiently large training sets.

  • QuantumKernel transpiles all circuits before execution. However, this

    information was not being passed, which calls the transpiler many times during the execution of the QSVC/QSVR algorithm. Now, had_transpiled=True is passed correctly and the algorithm runs faster.

  • QuantumKernel now provides an interface for users to specify a new class field, user_parameters. User parameters are an array of Parameter objects corresponding to parameterized quantum gates in the feature map circuit the user wishes to tune. This is useful in algorithms where feature map parameters must be bound and re-bound many times (i.e. variational algorithms). Users may also use a new function assign_user_parameters to assign real values to some or all of the user parameters in the feature map.

  • Introduce the TorchRuntimeClient for training a quantum model or a hybrid quantum-classical model faster using Qiskit Runtime. It can also be used for predicting the result using the trained model or calculating the score of the trained model faster using Qiskit Runtime.

Known Issues

  • If positional arguments are passed into QSVR or QSVC and these classes are printed, an exception is raised.

Deprecation Notes

  • Positional arguments in QSVR and QSVC are deprecated.

Bug Fixes

  • Fixed a bug in QuantumKernel where for statevector simulator all circuits were constructed and transpiled at once, leading to high memory usage. Now the circuits are batched similarly to how it was previously done for non-statevector simulators (same flag is used for both now; previously batch_size was silently ignored by statevector simulator)

  • Fix a bug where TorchConnector failed on backward pass computation due to empty parameters for inputs or weights. Validation added to qiskit_machine_learning.neural_networks.NeuralNetwork._validate_backward_output().

  • TwoLayerQNN now passes the value of the exp_val parameter in the constructor to the constructor of OpflowNN which TwoLayerQNN inherits from.

  • In some configurations forward pass of a neural network may return the same value across multiple calls even if different weights are passed. This behavior is confirmed with AQGD optimizer. This was due to a bug in the implementation of the objective functions. They cache a value obtained at the forward pass to be re-used in the backward pass. Initially, this cache was based on an identifier (a call of id() function) of the weights array. AQGD re-uses the same array for weights: it updates the values keeping an instance of the array the same. This caused to re-use the same forward pass value across all iteration. Now the forward pass cache is based on actual values of weights instead of identifiers.

  • Fix a bug, where qiskit_machine_learning.circuit.library.RawFeatureVector.copy() didn’t copy all internal settings which could lead to issues with the copied circuit. As a consequence qiskit_machine_learning.circuit.library.RawFeatureVector.bind_parameters() is also fixed.

  • Fixes a bug where VQC could not be instantiated unless either feature_map or ansatz were provided (#217). VQC is now instantiated with the default feature_map and/or ansatz.

  • The QNN weight parameter in TorchConnector is now registered in the torch DAG as weight, instead of _weights. This is consistent with the PyTorch naming convention and the weight property used to get access to the computed weights.

0.2.0

New Features

  • A base class TrainableModel is introduced for machine learning models. This class follows Scikit-Learn principles and makes the quantum machine learning compatible with classical models. Both NeuralNetworkClassifier and NeuralNetworkRegressor extend this class. A base class ObjectiveFunction is introduced for objective functions optimized by machine learning models. There are three objective functions introduced that are used by ML models: BinaryObjectiveFunction, MultiClassObjectiveFunction, and OneHotObjectiveFunction. These functions are used internally by the models.

  • The optimizer argument for the classes NeuralNetworkClassifier and NeuralNetworkRegressor, both of which extends the TrainableModel class, is made optional with the default value being SLSQP(). The same is true for the classes VQC and VQR as they inherit from NeuralNetworkClassifier and NeuralNetworkRegressor respectively.

  • The constructor of NeuralNetwork, and all classes that inherit from it, has a new parameter input_gradients which defaults to False. Previously this parameter could only be set using the setter method. Note that TorchConnector previously set input_gradients of the NeuralNetwork it was instantiated with to True. This is not longer the case. So if you use TorchConnector and want to compute the gradients w.r.t. the input, make sure you set input_gradients=True on the NeuralNetwork before passing it to TorchConnector.

  • Added a parameter initial_point to the neural network classifiers and regressors. This an array that is passed to an optimizer as an initial point to start from.

  • Computation of gradients with respect to input data in the backward method of NeuralNetwork is now optional. By default gradients are not computed. They may inspected and turned on, if required, by getting or setting a new property input_gradients in the NeuralNetwork class.

  • Now NeuralNetworkClassifier extends ClassifierMixin and NeuralNetworkRegressor extends RegressorMixin from Scikit-Learn and rely on their methods for score calculation. This also adds an ability to pass sample weights as an optional parameter to the score methods.

Deprecation Notes

  • The valid values passed to the loss argument of the TrainableModel constructor were partially deprecated (i.e. loss='l1' is replaced with loss='absolute_error' and loss='l2' is replaced with loss='squared_error'). This affects instantiation of classes like the NeuralNetworkClassifier. This change was made to reduce confusion that stems from using lowercase ‘l’ character which can be mistaken for a numeric ‘1’ or capital ‘I’. You should update your model instantiations by replacing ‘l1’ with ‘absolute_error’ and ‘l2’ with ‘squared_error’.

  • The weights property in TorchConnector is deprecated in favor of the weight property which is PyTorch compatible. By default, PyTorch layers expose weight properties to get access to the computed weights.

Bug Fixes

  • This fixes the exception that occurs when no optimizer argument is passed to NeuralNetworkClassifier and NeuralNetworkRegressor.

  • Fixes the computation of gradients in TorchConnector when a batch of input samples is provided.

  • TorchConnector now returns the correct input gradient dimensions during the backward pass in hybrid nn training.

  • Added a dedicated handling of ComposedOp as a operator in OpflowQNN. In this case output shape is determined from the first operator in the ComposedOp instance.

  • Fix the dimensions of the gradient in the quantum generator for the qGAN training.